# Event Detection in Videos: A Framework for the Development of New Methods

arXiv: https://arxiv.org/abs/2607.04372 · HTML: https://mlsanity.com/paper/2607.04372/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 21 of its 94 references that are in the map.
- First posted on arXiv 5 Jul 2026.

## Builds on (most cited first, 21 of 21)

- [CARLA: An Open Urban Driving Simulator](https://mlsanity.com/paper/1711.03938/) (2017)
- [Model Cards for Model Reporting](https://mlsanity.com/paper/1810.03993/) (2018)
- [Recognition in Terra Incognita](https://mlsanity.com/paper/1807.04975/) (2018)
- [Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation](https://mlsanity.com/paper/2010.16061/) (2020)
- [SoccerNet: A Scalable Dataset for Action Spotting in Soccer Videos](https://mlsanity.com/paper/1804.04527/) (2018)
- [Real-world Underwater Enhancement: Challenges, Benchmarks, and Solutions](https://mlsanity.com/paper/1901.05320/) (2019)
- [MOTSynth: How Can Synthetic Data Help Pedestrian Detection and Tracking?](https://mlsanity.com/paper/2108.09518/) (2021)
- [Video Processing from Electro-optical Sensors for Object Detection and Tracking in Maritime Environment: A Survey](https://mlsanity.com/paper/1611.05842/) (2016)
- [Fast image-based obstacle detection from unmanned surface vehicles](https://mlsanity.com/paper/1503.01918/) (2015)
- [BSUV-Net 2.0: Spatio-Temporal Data Augmentations for Video-Agnostic Supervised Background Subtraction](https://mlsanity.com/paper/2101.09585/) (2021)
- [Towards Benchmarking Scene Background Initialization](https://mlsanity.com/paper/1506.04051/) (2015)
- [A Fusion Framework for Camouflaged Moving Foreground Detection in the Wavelet Domain](https://mlsanity.com/paper/1804.05984/) (2018)
- [Foreground Detection in Camouflaged Scenes](https://mlsanity.com/paper/1707.03166/) (2017)
- [MassMIND: Massachusetts Maritime INfrared Dataset](https://mlsanity.com/paper/2209.04097/) (2022)
- [Summarizing the performances of a background subtraction algorithm measured on several videos](https://mlsanity.com/paper/2002.05654/) (2020)
- [The Tile: A 2D Map of Ranking Scores for Two-Class Classification](https://mlsanity.com/paper/2412.04309/) (2024)
- [A Hitchhiker's Guide to Understanding Performances of Two-Class Classifiers](https://mlsanity.com/paper/2412.04377/) (2024)
- [A Methodology to Evaluate Strategies Predicting Rankings on Unseen Domains](https://mlsanity.com/paper/2505.15595/) (2025)
- [Modality-Aware Infrared and Visible Image Fusion with Target-Aware Supervision](https://mlsanity.com/paper/2509.11476/) (2025)
- [What Is the Optimal Ranking Score Between Precision and Recall? We Can Always Find It and It Is Rarely F1](https://mlsanity.com/paper/2511.22442/) (2025)
- [Multi-domain performance analysis with scores tailored to user preferences](https://mlsanity.com/paper/2512.08715/) (2025)

## Abstract (arXiv)

Event detection tasks in videos, the most important aspect of video surveillance, aim to detect events either at the pixel-level, frame-level, or clip-level. Plenty of methods intended for event detection in different environments, for various applications, and within different acquisition techniques were introduced. Naturally, the attempts were made as well to classify these algorithms in terms of detection of performance or in terms of real-time abilities. Nevertheless, the lack of a large-scale dataset as well as rigorous performance evaluation methods have biased such comparisons as well as the development of the methods. Given the diversity of existing approaches, we believe it is essential for researchers to position their work within such a rich landscape. Thus, we propose a rigorous framework for developing new methods in event detection for videos. Specifically, this framework is based on three main pillars: datasets, performance evaluation, and scenarios for deploying methods.